Top 3 Data Strategy Mistakes (and How To Avoid Them) – Part 2 of 5
In Part 1 of this series I outlined what a Data Strategy should generally consist of and suggested that spotting a good one is easy. In this post, I introduce the first of three common mistakes, how to spot it and how to avoid it in order to deliver a better strategy.
Please read my Disclaimer by clicking here.
In the most extreme cases, I've read Data Strategies that talk a lot about the capabilities and structures and technologies that need to be deployed, without saying a single thing about why, or what these new things need to be put in place to achieve.
If the Business Outcomes of your Strategy are not clearly articulated in its first few pages, then I'm afraid that I'd be willing to bet that your strategy is a dud, without even needing to read the rest of what it is proposing.
Whilst many Data Strategies describe the same foundational things that need to be done (and rightfully so); everything you do, including the order in which you do things and how you do them, need to be very clearly linked to the reason that you're doing them. Any executive that has ever written or approved a business case will know that this is an obvious and necessary requirement before any money is invested and the proposals within a Data Strategy are no different.
The best Data Strategies start with a summary of an organisation's Business Strategy and the key outcomes that the Business is trying to achieve, before building out how the Data Strategy will directly support and accelerate those target outcomes. Depending on the scope and scale of the Strategy, these outcomes might be the ones that have been set at the top of the organisation, or might be those set at a more localised departmental level, but at whatever level they are set, referencing them is an absolute must, to ensure clarity in how the proposed actions will work to underpin whatever it is that the business is trying to do.
Please read my Disclaimer by clicking here.
Mistake #1: Unclear (or missing) Business Outcomes
The first, most glaringly obvious sign to look out for when reviewing a Data Strategy is where the Strategy's business outcomes are articulated.In the most extreme cases, I've read Data Strategies that talk a lot about the capabilities and structures and technologies that need to be deployed, without saying a single thing about why, or what these new things need to be put in place to achieve.
If the Business Outcomes of your Strategy are not clearly articulated in its first few pages, then I'm afraid that I'd be willing to bet that your strategy is a dud, without even needing to read the rest of what it is proposing.
Whilst many Data Strategies describe the same foundational things that need to be done (and rightfully so); everything you do, including the order in which you do things and how you do them, need to be very clearly linked to the reason that you're doing them. Any executive that has ever written or approved a business case will know that this is an obvious and necessary requirement before any money is invested and the proposals within a Data Strategy are no different.
The best Data Strategies start with a summary of an organisation's Business Strategy and the key outcomes that the Business is trying to achieve, before building out how the Data Strategy will directly support and accelerate those target outcomes. Depending on the scope and scale of the Strategy, these outcomes might be the ones that have been set at the top of the organisation, or might be those set at a more localised departmental level, but at whatever level they are set, referencing them is an absolute must, to ensure clarity in how the proposed actions will work to underpin whatever it is that the business is trying to do.
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